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Robert Fildes

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Hamid Abdollahyan, https://orcid.org/0000-0003-4852-5300

University of Tehran

Kansas State University

Barbara Neuhofer

Salzburg University of Applied Sciences

Hanken School of Economics

Ute Günkel-Maschek

Universität Heidelberg

University of Warwick

Kamran Razmdoost

ESCP Europe

Lyudmila S Mihaylova

The University of Sheffield

Daniel J . Flint

University of Tennessee Knoxville

Dragos Simandan

Brock University

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Papers by Robert Fildes

Analysis of judgmental adjustments in the presence of promotions

International Journal of Forecasting, Apr 1, 2013

Sales forecasting is increasingly complex due to a range of factors, such as the shortening of pr... more Sales forecasting is increasingly complex due to a range of factors, such as the shortening of product life cycles, increasingly competitive markets, and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers then add information to the forecast, such as future promotions, potentially improving the accuracy. Despite the importance of judgment and promotions, papers devoted to studying their relationship with forecasting performance are scarce. We analyze the accuracy of managerial adjustments in periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models.

The process of using a forecasting support system

International Journal of Forecasting, Jul 1, 2007

The actions of individual users of an experimental demand forecasting support system were traced ... more The actions of individual users of an experimental demand forecasting support system were traced and analyzed. Users adopted a wide variety of strategies when choosing a statistical forecasting method and deciding whether to apply a judgmental adjustment to its forecast. This was the case despite the users reporting similar levels of familiarity with statistical methods. However, the analysis also revealed that users were very consistent in the strategies that they applied across twenty different series. In general, the study found that users did not emulate mechanical forecasting systems in that they often did not choose the forecasting method that provided the best fit to past data. They also tended to examine only a small number of methods before making a selection, though they were likely to examine more methods when they perceived the series to be difficult to forecast Individuals who were relatively unsuccessful in identifying a well fitting statistical method tended to compensate for this by making large judgmental adjustments to the statistical forecasts. However, this generally led to forecasts that were less accurate than those produced by people who selected well fitting methods in the first place. These results should be of particular interest to designers of forecasting support systems who will typically have some stylised representation of the way that users employ their system to generate forecasts.

A retail store SKU promotions optimization model for category multi-period profit maximization

European Journal of Operational Research, Jul 1, 2017

Consumer promotions are an important element of competitive dynamics in retail markets and make a... more Consumer promotions are an important element of competitive dynamics in retail markets and make a significant difference in the retailer's profits. But no study has so far included all the elements that are required to meet retail business objectives. We extend the existing literatures by considering all the basic requirements for a promotional Decision Support System (DSS): reliance on operational (store-level) data only, the ability to predict sales as a function of prices and the inclusion of other promotional variables affecting the category. The new model delivers an optimizing promotional schedule at Stock-Keeping-Unit (SKU) level which maximizes multi-period category level profit under the constraints of business rules typically applied in practice. We first develop a high dimensional distributed lag demand model which integrates both cross-SKU competitive promotion information and cross-period promotional influences. We estimate the model by proposing a two stage sign constrained regularization approach to ensure realistic promotional parameters. Based on the demand model, we then build a nonlinear integer programming model to maximize the retailer's category profits over a planning horizon under constraints that model important business rules. The output of the model provides optimized prices, display and feature advertising planning together with sales and profit forecasts. Empirical tests over a number of stores and categories using supermarket data suggest that our model generates accurate sales forecasts and increases category profits by approximately 17% and that including cross-item and cross-period effects is also valuable.

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

European Journal of Operational Research, Feb 1, 2016

All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without ... more

Forecasting third-party mobile payments with implications for customer flow prediction

International Journal of Forecasting, Jul 1, 2020

Forecasting customer flow is the key for retailers when making their daily operational decisions,... more Forecasting customer flow is the key for retailers when making their daily operational decisions, but small retailers are often lack resources to obtain such forecasts. Instead of forecasting stores' total customer flows, this research utilizes the newly emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be further utilized to derive retailers' total customer flows indirectly, thereby overcoming the constraints small retailers face. We propose a third party mobile-payment platform centered daily mobile payments forecasting solution based on an extension of the newly developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasting for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT together with a strategy for multi-period ahead forecasting provides more accurate forecasts compared with established benchmarks. Pooling data from the platform across stores leads to benefits compared to analyzing the data individually, demonstrating the value of this machine learning application.

Retail forecasting: Research and practice

International Journal of Forecasting, Oct 1, 2022

This paper first introduces the forecasting problems faced by large retailers, from the strategic... more This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

Retail sales forecasting with meta-learning

European Journal of Operational Research, 2021

Retail sales forecasting often requires forecasts for thousands of products for many stores. We p... more Retail sales forecasting often requires forecasts for thousands of products for many stores. We present a meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods. The experiments which are based on IRI weekly data show that the proposed meta learner provides superior forecasting performance compared with a number of state-of-art benchmarks, though the accuracy gains over some more sophisticated meta ensemble benchmarks are modest and the learnt features lack interpretability. When designing a meta-learner in forecasting retail sales, we recommend building a pool of base-forecasters including both individual and pooled forecasting methods, and target finding the best combination forecasts instead of the best individual method.

Engaging research with practice — An invited editorial

International Journal of Forecasting, 2021

An age dependent branching process with variable lifetime distribution

Advances in Applied Probability, Dec 1, 1972

A branching process with variable lifetime distribution is defined by a sequence of distribution ... more A branching process with variable lifetime distribution is defined by a sequence of distribution functions {Gi(t)}, together with a probability generating function, h(s) = Σk∞= 0pksk. An ith generation particle lives a random length of time, determined by Gi(t). At the end of a particle's life it produces children, the number being determined by h(s). These offspring behave like the initial particle except they are (i + 1)th generation particles and have lifetime distribution Gi + 1 (t).Let Zi(t) be the number of particles alive at time t, the initial particle being born into the ith generation. Integral equations are derived for the moments of Zi(t) and it is shown that for some constants Ni, γ, a, Zi (t)/(Nitγ-1eαt) converges in mean square to a proper random variable.

World index of economic forecasts

Long Range Planning, Dec 1, 1979

Correspondence on the selection of error measures for comparisons among forecasting methods

Journal of Forecasting, 1995

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an im... more Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.

Judgmental forecasts of time series affected by special events: does providing a statistical forecast improve accuracy?

Journal of Behavioral Decision Making, Mar 1, 1999

Time series found in areas such as marketing and sales often have regular established patterns wh... more

Engaging research with practice — An invited editorial

International Journal of Forecasting, Jul 1, 2021

Forecasting in supply chain companies: Should you trust your judgment?

OR Insight, May 18, 2011

Making progress in forecasting

International Journal of Forecasting, 2006

Twenty-five years ago, the International Institute of Forecasters was established "to bridge the ... more Twenty-five years ago, the International Institute of Forecasters was established "to bridge the gap between theory and practice." Its primary vehicle was the Journal of Forecasting and is now the International Journal of Forecasting. The Institute emphasizes empirical comparisons of reasonable forecasting approaches. Such studies can be used to identify the best forecasting procedures to use under given conditions, a process we call evidence-based forecasting. Unfortunately, evidence-based forecasting meets resistance from academics and practitioners when the findings differ from currently accepted beliefs. As a consequence, although much progress has been made in developing improved forecasting methods, the diffusion of useful forecasting methods has been disappointing. To bridge the gap between theory and practice, we recommend a stronger emphasis on the method of multiple hypotheses and on invited replications of important research. It is then necessary to translate the findings into principles that are easy to understand and apply. The Internet and software provide important opportunities for making the latest findings available to researchers and practitioners. Because researchers and practitioners believe that their areas are unique, we should organize findings so that they are relevant to each area and make them easily available when people search for information about forecasting in their area. Organisational barriers to change still remain to be overcome. Research into the specific issues faced when forecasting remains a priority.

Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting

Interfaces, Dec 1, 2007

Accurate forecasts are crucial to successful planning in many organizations and in 2001 forty int... more Accurate forecasts are crucial to successful planning in many organizations and in 2001 forty international experts published a set of principles to guide best practice in forecasting. Some of the principles relate to the use management judgment. Almost all organisations use judgment at some stage in their forecasting process, but do they do so effectively? While judgment can lead to significant improvements in forecasting accuracy, it can also suffer from biases and inconsistency. The principles therefore

A Note on the Robustness of Performance of Methods and Rankings for M4 Competition

Turkish journal of forecasting, Dec 31, 2020

M4 forecasting competition provided some useful information to forecasting literature. The provid... more M4 forecasting competition provided some useful information to forecasting literature. The provided information is based on calculated error metrics for ranking methods in the competition. The organizers of the competition calculated arithmetic mean as a descriptive statistic for evaluating the performance of all competitors. In this paper, the effect of different descriptive statistics for ranking of methods is investigated. It is found that the distribution of error metrics for competitor forecasting methods is not symmetric. Thus, the arithmetic mean descriptive statistic is not a good metric to determine the centre of nonsymmetric distributions and it will not well present centre of the distribution. In this study, it is showed that the median will well present centre of distribution for error metrics of competitor forecasting methods. When the median is used as a descriptive statistic for ranking methods, the ranks of methods is different form ranks which are calculated according to the arithmetic mean descriptive statistics. Moreover, the direction accuracy metric is calculated for the best ten methods in the competition. So, the forecasting methods are ranked according to direction accuracy and it is showed that the ranks are different from the competition results.

Learning from forecasting competitions

International Journal of Forecasting, 2019

The objectives of the M4 (following on from the previous M-competitions) were to "learn how to im... more The objectives of the M4 (following on from the previous M-competitions) were to "learn how to improve the forecasting accuracy" and investigate "how such learning can be applied to advance the theory and practice of forecasting". How successful have previous competitions been and can we expect anything different from the latest one? The early forecasting competitions, starting with Newbold and Granger (1974), provided a shock to the time series and statistical forecasting

Forecasting retailer product sales in the presence of structural change

European Journal of Operational Research, 2019

Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectiv... more Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectively manage their inventory. Previous studies have proposed forecasting methods which incorporate the effect of various marketing activities including prices and promotions. However, their methods have overlooked that the effects of the marketing activities on product sales may change over time. Therefore, these methods may be subject to the structural change problem and generate biased and less accurate forecasts. In this study, we propose more effective methods to forecast retailer product sales which take into account the problem of structural change. Based on data from a well-known US retailer, we show that our methods outperform conventional forecasting methods that ignore the possibility of such changes.

Forecasting Systems for Production and Inventory Control

International Journal of Operations & Production Management, 1992

Quantitative forecasting techniques see their greatest application as part of production and inve... more Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many ...

Analysis of judgmental adjustments in the presence of promotions

International Journal of Forecasting, Apr 1, 2013

Sales forecasting is increasingly complex due to a range of factors, such as the shortening of pr... more Sales forecasting is increasingly complex due to a range of factors, such as the shortening of product life cycles, increasingly competitive markets, and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers then add information to the forecast, such as future promotions, potentially improving the accuracy. Despite the importance of judgment and promotions, papers devoted to studying their relationship with forecasting performance are scarce. We analyze the accuracy of managerial adjustments in periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models.

The process of using a forecasting support system

International Journal of Forecasting, Jul 1, 2007

The actions of individual users of an experimental demand forecasting support system were traced ... more The actions of individual users of an experimental demand forecasting support system were traced and analyzed. Users adopted a wide variety of strategies when choosing a statistical forecasting method and deciding whether to apply a judgmental adjustment to its forecast. This was the case despite the users reporting similar levels of familiarity with statistical methods. However, the analysis also revealed that users were very consistent in the strategies that they applied across twenty different series. In general, the study found that users did not emulate mechanical forecasting systems in that they often did not choose the forecasting method that provided the best fit to past data. They also tended to examine only a small number of methods before making a selection, though they were likely to examine more methods when they perceived the series to be difficult to forecast Individuals who were relatively unsuccessful in identifying a well fitting statistical method tended to compensate for this by making large judgmental adjustments to the statistical forecasts. However, this generally led to forecasts that were less accurate than those produced by people who selected well fitting methods in the first place. These results should be of particular interest to designers of forecasting support systems who will typically have some stylised representation of the way that users employ their system to generate forecasts.

A retail store SKU promotions optimization model for category multi-period profit maximization

European Journal of Operational Research, Jul 1, 2017

Consumer promotions are an important element of competitive dynamics in retail markets and make a... more Consumer promotions are an important element of competitive dynamics in retail markets and make a significant difference in the retailer's profits. But no study has so far included all the elements that are required to meet retail business objectives. We extend the existing literatures by considering all the basic requirements for a promotional Decision Support System (DSS): reliance on operational (store-level) data only, the ability to predict sales as a function of prices and the inclusion of other promotional variables affecting the category. The new model delivers an optimizing promotional schedule at Stock-Keeping-Unit (SKU) level which maximizes multi-period category level profit under the constraints of business rules typically applied in practice. We first develop a high dimensional distributed lag demand model which integrates both cross-SKU competitive promotion information and cross-period promotional influences. We estimate the model by proposing a two stage sign constrained regularization approach to ensure realistic promotional parameters. Based on the demand model, we then build a nonlinear integer programming model to maximize the retailer's category profits over a planning horizon under constraints that model important business rules. The output of the model provides optimized prices, display and feature advertising planning together with sales and profit forecasts. Empirical tests over a number of stores and categories using supermarket data suggest that our model generates accurate sales forecasts and increases category profits by approximately 17% and that including cross-item and cross-period effects is also valuable.

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

European Journal of Operational Research, Feb 1, 2016

All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without ... more

Forecasting third-party mobile payments with implications for customer flow prediction

International Journal of Forecasting, Jul 1, 2020

Forecasting customer flow is the key for retailers when making their daily operational decisions,... more Forecasting customer flow is the key for retailers when making their daily operational decisions, but small retailers are often lack resources to obtain such forecasts. Instead of forecasting stores' total customer flows, this research utilizes the newly emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be further utilized to derive retailers' total customer flows indirectly, thereby overcoming the constraints small retailers face. We propose a third party mobile-payment platform centered daily mobile payments forecasting solution based on an extension of the newly developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasting for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT together with a strategy for multi-period ahead forecasting provides more accurate forecasts compared with established benchmarks. Pooling data from the platform across stores leads to benefits compared to analyzing the data individually, demonstrating the value of this machine learning application.

Retail forecasting: Research and practice

International Journal of Forecasting, Oct 1, 2022

This paper first introduces the forecasting problems faced by large retailers, from the strategic... more This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

Retail sales forecasting with meta-learning

European Journal of Operational Research, 2021

Retail sales forecasting often requires forecasts for thousands of products for many stores. We p... more Retail sales forecasting often requires forecasts for thousands of products for many stores. We present a meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods. The experiments which are based on IRI weekly data show that the proposed meta learner provides superior forecasting performance compared with a number of state-of-art benchmarks, though the accuracy gains over some more sophisticated meta ensemble benchmarks are modest and the learnt features lack interpretability. When designing a meta-learner in forecasting retail sales, we recommend building a pool of base-forecasters including both individual and pooled forecasting methods, and target finding the best combination forecasts instead of the best individual method.

Engaging research with practice — An invited editorial

International Journal of Forecasting, 2021

An age dependent branching process with variable lifetime distribution

Advances in Applied Probability, Dec 1, 1972

A branching process with variable lifetime distribution is defined by a sequence of distribution ... more A branching process with variable lifetime distribution is defined by a sequence of distribution functions {Gi(t)}, together with a probability generating function, h(s) = Σk∞= 0pksk. An ith generation particle lives a random length of time, determined by Gi(t). At the end of a particle's life it produces children, the number being determined by h(s). These offspring behave like the initial particle except they are (i + 1)th generation particles and have lifetime distribution Gi + 1 (t).Let Zi(t) be the number of particles alive at time t, the initial particle being born into the ith generation. Integral equations are derived for the moments of Zi(t) and it is shown that for some constants Ni, γ, a, Zi (t)/(Nitγ-1eαt) converges in mean square to a proper random variable.

World index of economic forecasts

Long Range Planning, Dec 1, 1979

Correspondence on the selection of error measures for comparisons among forecasting methods

Journal of Forecasting, 1995

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an im... more Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.

Judgmental forecasts of time series affected by special events: does providing a statistical forecast improve accuracy?

Journal of Behavioral Decision Making, Mar 1, 1999

Time series found in areas such as marketing and sales often have regular established patterns wh... more

Engaging research with practice — An invited editorial

International Journal of Forecasting, Jul 1, 2021

Forecasting in supply chain companies: Should you trust your judgment?

OR Insight, May 18, 2011

Making progress in forecasting

International Journal of Forecasting, 2006

Twenty-five years ago, the International Institute of Forecasters was established "to bridge the ... more Twenty-five years ago, the International Institute of Forecasters was established "to bridge the gap between theory and practice." Its primary vehicle was the Journal of Forecasting and is now the International Journal of Forecasting. The Institute emphasizes empirical comparisons of reasonable forecasting approaches. Such studies can be used to identify the best forecasting procedures to use under given conditions, a process we call evidence-based forecasting. Unfortunately, evidence-based forecasting meets resistance from academics and practitioners when the findings differ from currently accepted beliefs. As a consequence, although much progress has been made in developing improved forecasting methods, the diffusion of useful forecasting methods has been disappointing. To bridge the gap between theory and practice, we recommend a stronger emphasis on the method of multiple hypotheses and on invited replications of important research. It is then necessary to translate the findings into principles that are easy to understand and apply. The Internet and software provide important opportunities for making the latest findings available to researchers and practitioners. Because researchers and practitioners believe that their areas are unique, we should organize findings so that they are relevant to each area and make them easily available when people search for information about forecasting in their area. Organisational barriers to change still remain to be overcome. Research into the specific issues faced when forecasting remains a priority.

Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting

Interfaces, Dec 1, 2007

Accurate forecasts are crucial to successful planning in many organizations and in 2001 forty int... more Accurate forecasts are crucial to successful planning in many organizations and in 2001 forty international experts published a set of principles to guide best practice in forecasting. Some of the principles relate to the use management judgment. Almost all organisations use judgment at some stage in their forecasting process, but do they do so effectively? While judgment can lead to significant improvements in forecasting accuracy, it can also suffer from biases and inconsistency. The principles therefore

A Note on the Robustness of Performance of Methods and Rankings for M4 Competition

Turkish journal of forecasting, Dec 31, 2020

M4 forecasting competition provided some useful information to forecasting literature. The provid... more M4 forecasting competition provided some useful information to forecasting literature. The provided information is based on calculated error metrics for ranking methods in the competition. The organizers of the competition calculated arithmetic mean as a descriptive statistic for evaluating the performance of all competitors. In this paper, the effect of different descriptive statistics for ranking of methods is investigated. It is found that the distribution of error metrics for competitor forecasting methods is not symmetric. Thus, the arithmetic mean descriptive statistic is not a good metric to determine the centre of nonsymmetric distributions and it will not well present centre of the distribution. In this study, it is showed that the median will well present centre of distribution for error metrics of competitor forecasting methods. When the median is used as a descriptive statistic for ranking methods, the ranks of methods is different form ranks which are calculated according to the arithmetic mean descriptive statistics. Moreover, the direction accuracy metric is calculated for the best ten methods in the competition. So, the forecasting methods are ranked according to direction accuracy and it is showed that the ranks are different from the competition results.

Learning from forecasting competitions

International Journal of Forecasting, 2019

The objectives of the M4 (following on from the previous M-competitions) were to "learn how to im... more The objectives of the M4 (following on from the previous M-competitions) were to "learn how to improve the forecasting accuracy" and investigate "how such learning can be applied to advance the theory and practice of forecasting". How successful have previous competitions been and can we expect anything different from the latest one? The early forecasting competitions, starting with Newbold and Granger (1974), provided a shock to the time series and statistical forecasting

Forecasting retailer product sales in the presence of structural change

European Journal of Operational Research, 2019

Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectiv... more Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectively manage their inventory. Previous studies have proposed forecasting methods which incorporate the effect of various marketing activities including prices and promotions. However, their methods have overlooked that the effects of the marketing activities on product sales may change over time. Therefore, these methods may be subject to the structural change problem and generate biased and less accurate forecasts. In this study, we propose more effective methods to forecast retailer product sales which take into account the problem of structural change. Based on data from a well-known US retailer, we show that our methods outperform conventional forecasting methods that ignore the possibility of such changes.

Forecasting Systems for Production and Inventory Control

International Journal of Operations & Production Management, 1992

Quantitative forecasting techniques see their greatest application as part of production and inve... more Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many ...